Robust Localization in Large-Scale Symmetric Environments through Dynamic Topological Mapping

Abstract

Visual place recognition in large-scale, indoor environments often suffers from perceptual aliasing due to structural symmetries and dynamic changes. This work presents a robust hierarchical topological mapping framework designed for long-term robot autonomy. Our system integrates multi-modal data (including 2D LiDAR, odometry, and RGB imagery) into a two-layer architecture. First, a Layout Layer is designed to capture the geometric structure of the environment. Then, a Visual Layer is used to encode image sequences. A key contribution is the dynamic map maintenance mechanism, which monitors the attenuation of edge weights to detect environmental transitions, such as the opening or closing of doors. This allows for seamless lifelong updates without human intervention in large-scale environments. We evaluate our approach using various visual descriptors (eg SuperGlue, Patch-NetVLAD, and SeqVLAD) within a sequence-based matching pipeline. Experimental results in a 750 m^2 real-world facility demonstrate that the proposed method achieves high discrimination and scalability, even in challenging open areas and symmetric corridors. This framework provides a reliable solution for assistive robotics navigating complex, evolving public spaces.